English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We develop generalization error bounds for stochastic gradient descent (SGD)with label noise in non-convex settings under uniform dissipativity andsmoothness conditions. Under a suitable choice of semimetric, we establish acontraction in Wasserstein distance of the label noise stochastic gradient flowthat depends polynomially on the parameter dimension $d$. Using the frameworkof algorithmic stability, we derive time-independent generalisation errorbounds for the discretized algorithm with a constant learning rate. The errorbound we achieve scales polynomially with $d$ and with the rate of $n^{-2/3}$,where $n$ is the sample size. This rate is better than the best-known rate of$n^{-1/2}$ established for stochastic gradient Langevin dynamics (SGLD) --which employs parameter-independent Gaussian noise -- under similar conditions.Our analysis offers quantitative insights into the effect of label noise.

Generalization Bounds for Label Noise Stochastic Gradient Descent